The Global Impact of Microbial Pathogens

Global number of deaths (A) and Years of Life Lost (B), by pathogen and infectious syndrome, in 2019 Adapted from Ikuta et al., 2022, on behalf of GBD 2019 Antimicrobial Resistance Collaborators 

The Global Impact of Microbial Pathogens

World Health Organisation Global Priority Pathogens list. This catalogue includes, besides Mycobacterium tuberculosis considered the number one global priority, a list of twelve microorganisms grouped under three priority tiers according to their antimicrobial resistance: critical (Acinetobacter baumannii, Pseudomonas aeruginosa and Enterobacteriaceae), high (Enterococcus faecium, Helicobacter pylori, Salmonella species, Staphylococcus aureus, Campylobacter species and Neisseria gonorrhoeae), and medium (Streptococcus pneumoniae, Haemophilus influenzae and Shigella species). The major objective was to encourage the prioritisation of funding and incentives, align research and development priorities of public health relevance, and garner global coordination in the fight against antimicrobial-resistant bacteria. Adapted from World Health Organization, 2017.

The Global Impact of Microbial Pathogens

Clinical microbiology can be defined as the characterisation of pathogen samples to direct the management of individual infected patients (diagnostics) and monitor the epidemiology of infectious diseases (public health).

The Global Impact of Microbial Pathogens

Bacterial Population Genetics

Pathogenesis and Natural History of Infection

Outbreak Investigation and Control

Surveillance of Infectious Diseases

A Genomic Approach to Clinical Microbiology

Principles of current processing of bacterial pathogens. Schematic representation of the current workflow for processing samples for bacterial pathogens is presented, with high complexity and a typical timescale of a few weeks to a few months. Samples that are likely to be normally sterile are often cultured on rich medium that will support the growth of any culturable organism. Samples contaminated with colonising flora present a challenge for growing the infecting pathogen. Many types of culture media (referred to as selective media) are used to favour the growth of the suspected pathogen. Once an organism is growing, the likely pathogens are then processed through a complex pathway that has many contingencies to determine species and antimicrobial susceptibility. Broadly, there are two approaches. One approach uses MALDI-TOF for species identification prior to setting up susceptibility testing. The other uses Gram staining followed by biochemical testing to determine species; susceptibility testing is often set up simultaneously with doing biochemical tests. Lastly, depending on the species and perceived likelihood of an outbreak, a small subset of isolates may be chosen for further investigation using a wide range of typing tests. Adapted from Didelot et al., 2012

A Genomic Approach to Clinical Microbiology

The three revolutions in sequencing technology that have transformed the landscape of bacterial genome sequencing. The first-generation, also known as Sanger sequencers, is represented by the ABI Capillary Sequencer (Applied Biosystems). The second-generation, also known as high-throughput sequencers, is represented by the MiSeq, a 4-channel sequencer, and the NextSeq, a 2-channel sequencer (Illumina), both sequencing by synthesis instruments. These instruments allow the sequencing of both ends of the DNA fragment. Lastly, the third-generation, also known as long-read sequencers, is represented by Pacific Bioscience BS sequencer and Oxford Nanopore MinION sequencer. Adapted from Hagemann, 2015; Nicholas J. Loman and Pallen, 2015; Goodwin et al., 2016; Wang et al., 2021; Metzker, 2010; Xu et al., 2020.

A Genomic Approach to Clinical Microbiology

Principles of current processing of bacterial pathogens based on whole-genome sequencing. Schematic representation of the workflow for processing samples for bacterial pathogens after the adoption of whole-genome sequencing, with an expected timescale that could fit within a single day. The culture steps would be the same as currently used in a routine microbiology laboratory. Once a likely pathogen is ready for sequencing, DNA will be extracted, taking as little as 2 hours to prepare the DNA for sequencing. After sequencing, the main processes for yielding information will be computational. Automated sequence assembly algorithms are necessary for processing the raw sequence data, from which species, relationship to other isolates of the same species, antimicrobial resistance profile and virulence gene content can be assessed. All the results will also be used for outbreak detection and infectious diseases surveillance Adapted from Didelot et al., 2012

A Genomic Approach to Clinical Microbiology

Hypothetical workflow based on metagenomic sequencing. Schematic representation of the hypothetical workflow for the direct processing of samples from suspected sources of pathogens after adoption of metagenomic sequencing, with an expected timescale that could fit within a single day. Adapted from Didelot et al., 2012

The Role of Bioinformatics

Typical bioinformatic analysis procedure for metagenomic data.

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Part I

Part II

Part III

Part IV

• Critical steps in clinical shotgun metagenomics for the concomitant detection and typing of microbial pathogens*

• Detection of a novel mcr-5.4 gene variant in hospital tap water by shotgun metagenomic sequencing*

• DEN-IM: Dengue virus genotyping from shotgun and targeted metagenomics*

• LMAS: Last Metagenomic Assembler
  Standing*

• hAMRonization: Enhancing antimicrobial resistance prediction using PHA4GE standards and specification
• Future-proofing and maximising the utility of metadata: The PHA4GE SARS-CoV-2 contextual data spec*ification package

• Software testing in microbial bioinformatics: a call to action*

Part I

Applying metagenomics in the clinical context

Couto et al., 2018 Sci Rep DOI: https://doi.org/10.1038/s41598-018-31873-w

Fleres et al., 2019 JAC DOI: https://doi.org/10.1093/jac/dkz363

Mendes et al., 2020 Microbial Genomics DOI: https://doi.org/10.1099/mgen.0.000328

Precision, Sensibility and Performance

Scheme of the bioinformatic analysis of the metagenomics samples. In order to evaluate and compare the accuracy and reliability of the bioinformatics analyses in providing the closest results to culture and WGS of any cultured isolates, three different pipelines (two commercially and one freely available) were used (Fig. 1). Different tools to perform raw read quality control, filtering and trimming were used and reads were mapped against the human genome (hg19) before performing taxonomic classification. Reads mapping to hg19 were removed from the analysis to increase the efficiency of the bioinformatics tools. Typing (MLST), phylogenetic analysis, plasmid analysis, detection of antimicrobial resistance and virulence genes was performed. To determine the appropriateness of shotgun metagenomics as a predictor of the WGS (chromosome and plasmids), Shotgun metagenomics results obtained were compared with the results of WGS of any bacterial isolates obtained from culturing the sample. Source: Couto et al,. 2018.

Couto et al., 2018 Sci Rep DOI: https://doi.org/10.1038/s41598-018-31873-w

Precision, Sensibility and Performance

Couto et al., 2018 Sci Rep DOI: https://doi.org/10.1038/s41598-018-31873-w

Precision, Sensibility and Performance

Comparative analysis of the genetic environment of mcr-5 between the reference plasmid pSE13-SA01718 (accession no. KY807921.1) and the annotated hybrid metagenome contig (accession no. MK965519). The contig carrying the mcr-5.4 gene consists of the following putative gene products: 7-carboxy-7-deazaguanine synthase (queE), 7-cyano-7-deazaguanine synthase (queC), glycine cleavage system transcriptional antiactivator GcvR (gcvR), thiol peroxidase (tpx), sulphurtransferase TusA family protein (sirA), hypothetical protein (hp), truncated MFS-type transporter (Dmsf), lipid A phosphoethanolamine transferase (mcr-5.4), ChrB domain protein (chrB), transposon resolvase (tnpR) and truncated transposon transposase (DtnpA). Areas with 98% identity between sequences are represented in light grey. Arrows indicate the position and direction of the genes. The transposon Tn6452 sequence in the reference plasmid pSE13-SA01718 is bounded by inverted repeats: IRL and IRR.Source: Fleres et al., 2019.

Fleres et al., 2019 JAC DOI: https://doi.org/10.1093/jac/dkz363

Dengue Virus genomics

• DENV-1  - genotypes I-V​​

• DENV-2 - genotypes Asian I, Asian II, Cosmopolitan, American, Asian/American & Sylvatic​

• DENV-3 - genotypes I-V​

• DENV-4 - genotypes I - III & Sylvatic​

https://doi.org/10.1371/journal.pntd.0001876.g002

https://doi.org/10.1371/journal.pntd.0000757

Sequential infection increases the risk of a severe form of the infection - dengue hemorrhagic fever.

The DEN-IM Workflow

Requirements

A solution

Mendes et al., 2020 Microbial Genomics DOI: https://doi.org/10.1099/mgen.0.000328

https://github.com/B-UMMI/DEN-IM

The DEN-IM Workflow

Mendes et al., 2020 Microbial Genomics DOI: https://doi.org/10.1099/mgen.0.000328

The DEN-IM Workflow

Part II

Impact of de novo assemblers in metagenomics

Mendes et al., 2023 GigaScience DOI: https://doi.org/10.1093/gigascience/giac122

de novo Assembly

Approaches to de novo genome assembly. In Overlap, Layout, Consensus assembly, (1) overlaps are found between reads and an overlap graph constructed (edges indicate overlapping reads). (2) Reads are laid out into contigs based on the overlaps (lines indicate overlapping portions). (3) The most likely sequence is chosen to construct the consensus sequence. In the De Bruijn graph assembly, (1) reads are decomposed into kmers of a determined size by sliding a window of size k (in here of k=3) across the reads. (2) The kmers become vertices in the De Bruijn graph, with edges connecting overlapping kmers. Polymorphisms (red) form branches in the graph. A count is kept of how many times a kmer is seen, shown here as the numbers above kmers. (3) Contigs are built by walking the graph from the edge nodes. A variety of heuristics handle branches in the graphs—for example, low coverage paths, as shown here, may be ignored. Adapted from Ayling et al., 2020. 

The LMAS Workflow

The LMAS Workflow

The LMAS Workflow

The LMAS Workflow

• Eight bacterial genomes
• Four plasmids 
• Even and logarithmic distribution of species
• Simulated samples (with and without error)

Mendes et al., 2023 GigaScience DOI: https://doi.org/10.1093/gigascience/giac122

Mendes et al., 2023 GigaScience DOI: https://doi.org/10.1093/gigascience/giac122

Mendes et al., 2023 GigaScience DOI: https://doi.org/10.1093/gigascience/giac122

Part III

Griffiths et al., 2022 GigaScience DOI: https://doi.org/10.1093/gigascience/giac003

Challenges of data availability in metagenomics and beyond

Contextual Data Specification

The hAMRonization Utility

Contextual Data Specification

Griffiths et al., 2022 GigaScience DOI: https://doi.org/10.1093/gigascience/giac003

Part IV

van der Putten et al., 2022 Microbial Genomics DOI: https://doi.org/10.1099/mgen.0.000790

Crowdsourcing for software robustness in metagenomics and beyond

Improving Standards in the Community

van der Putten et al., 2022 Microbial Genomics DOI: https://doi.org/10.1099/mgen.0.000790